[HTML][HTML] Deep learning for network traffic monitoring and analysis (NTMA): A survey
Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
T-GCN: A temporal graph convolutional network for traffic prediction
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …
system and is of great significance for urban traffic planning, traffic management, and traffic …
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐
temporal dependencies at different scales. Recently, several hybrid deep learning models …
temporal dependencies at different scales. Recently, several hybrid deep learning models …
A3t-gcn: Attention temporal graph convolutional network for traffic forecasting
J Bai, J Zhu, Y Song, L Zhao, Z Hou, R Du… - … International Journal of …, 2021 - mdpi.com
Accurate real-time traffic forecasting is a core technological problem against the
implementation of the intelligent transportation system. However, it remains challenging …
implementation of the intelligent transportation system. However, it remains challenging …
Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)
The traffic state in an urban transportation network is determined via spatio-temporal traffic
propagation. In early traffic forecasting studies, time-series models were adopted to …
propagation. In early traffic forecasting studies, time-series models were adopted to …
Host load prediction in cloud computing with discrete wavelet transformation (dwt) and bidirectional gated recurrent unit (bigru) network
J Dogani, F Khunjush, M Seydali - Computer Communications, 2023 - Elsevier
Providing pay-as-you-go storage and computing services have contributed to the
widespread adoption of cloud computing. Using virtualization technology, cloud service …
widespread adoption of cloud computing. Using virtualization technology, cloud service …
Trafformer: unify time and space in traffic prediction
Traffic prediction is an important component of the intelligent transportation system. Existing
deep learning methods encode temporal information and spatial information separately or …
deep learning methods encode temporal information and spatial information separately or …
Network traffic prediction based on diffusion convolutional recurrent neural networks
By predicting the traffic load on network links, a network operator can effectively pre-dispose
resource-allocation strategies to early address, eg, an incoming congestion event. Traffic …
resource-allocation strategies to early address, eg, an incoming congestion event. Traffic …
Characteristics of co-allocated online services and batch jobs in internet data centers: a case study from Alibaba cloud
In order to reduce power and energy costs, giant cloud providers now mix online and batch
jobs on the same cluster. Although the co-allocation of such jobs improves machine …
jobs on the same cluster. Although the co-allocation of such jobs improves machine …
Predicting cycle-level traffic movements at signalized intersections using machine learning models
Predicting accurate traffic parameters is fundamental and cost-effective in providing traffic
applications with required information. Many studies adopted various parametric and …
applications with required information. Many studies adopted various parametric and …